Integration of Natural Language and Vision Processing:

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Language: English

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Unsupervised algorithms hold the greatest promise for achieving the scalability required because they do not require manually generated training data. When all the antecedent clauses of a rule are available in the database, the rule is fired, resulting in new inferences. After all, it is becoming apparent that empirical learning of Natural Language Processing (NLP) can alleviate NLP's all-time main problem, viz. the knowledge acquisition bottleneck: empirical ML methods such as rule induction, top down induction of decision trees, lazy learning, inductive logic programming, and some types of neural network learning, seem to be excellently suited to automatically induce exactly that knowledge that is hard to gather by hand.

Pages: 256

Publisher: Springer; Softcover reprint of the original 1st ed. 1996 edition (January 1, 1900)

ISBN: 9401072337

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